The present invention relates to a method for predicting a state of an object on the basis of dynamic image data and a computing device performing same, the method enabling initial dynamic image data and delay image data to be predicted by performing learning on the basis of dynamic image data captured at a time point when both blood flow image information and disease-specific biological information are included, and furthermore, enabling blood flow image information and disease-specific biological information of the object to be provided.

A method of establishing an enhanced three-dimensional (3D) model of intracranial angiography is provided and includes: obtaining a bright-blood image group, a black-blood image group and an enhanced black-blood image group; preprocessing image pairs to obtain first bright-blood images and black-blood images; registering the first bright-blood image by taking the first black-blood image as reference to obtain a registered bright-blood image group; eliminating flowing void artifact to obtain an artifact-elimination enhanced black-blood image group; subtracting each image of the artifact-elimination enhanced black-blood image group from corresponding black-blood image to obtain angiography enhanced images; establishing a blood 3D model and a vascular 3D model with blood boundary expansion by using the registered bright-blood image group; establishing an angiography enhanced 3D model by using the angiography enhanced images; obtaining an enhanced 3D model of intracranial angiography based on the blood 3D model, the vascular 3D model and the angiography enhanced 3D model.

In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

Devices, systems, and methods for visually depicting a vessel and evaluating a physiological condition of the vessel are disclosed. One embodiment includes obtaining, at a first time, a first image of the vessel, the image being in a first medical modality, and obtaining, at a second time subsequent to the first time, a second image of the vessel, the image being in the first medical modality. The method also includes spatially co-registering the first and second images and outputting a visual representation of the co-registered first and second images on a display. Further, the method includes determining a physiological difference between the vessel at the first time and the vessel at the second time based on the co-registered first and second images, and evaluating the physiological condition of the vessel of the patient based on the determined physiological difference.

One or more devices, systems, methods and storage mediums for optical imaging medical devices, and methods and storage mediums for use with same, for viewing, controlling, updating, and emphasizing one or more imaging modalities and/or for constructing or reconstructing 2D and/or 3D structure(s) are provided herein. One or more embodiments provide at least one intuitive Graphical User Interface (GUI), method, device, apparatus, system, or storage medium to comprehend information, including, but not limited to, molecular structure of a vessel, and to provide an ability to manipulate the vessel information and/or to construct or reconstruct 2D and/or 3D structure(s) of the vessel to improve or maximize accuracy in one or more images. In addition to controlling one or more imaging modalities, the GUI may operate for one or more applications, including, but not limited to, expansion/underexpansion (e.g., for a stent) and/or apposition/malapposition (e.g., for a stent), co-registration, and imaging.

A method for analyzing a patient based on a volumetric pulmonary scan includes receiving volumetric pulmonary scan data representative of a patient's pulmonary structure. This method also includes determining a level of transpulmonary pressure defining an effort metric based on one or more characteristics from the received volumetric pulmonary scan data. This method further includes determining one or more physiological or anatomical parameters associated with the transpulmonary pressure based on the received volumetric pulmonary scan data and the effort metric. A non-transitory computer readable medium can be programmed with instructions for causing one or more processors to perform the method for analyzing a patient based on a volumetric pulmonary scan.

An actual measurement value calculation unit calculates a first actual measurement value of oxygen saturation of a tissue to be observed. A reference value calculation unit calculates a first reference value of the oxygen saturation of the tissue to be observed. A relative value calculation unit calculates a relative value of the first actual measurement value with reference to the first reference value. An image generation unit generates an image of the relative value of the first actual measurement value on the basis of an evaluation color table to generate an evaluation oxygen-saturation image. A display unit displays the evaluation oxygen-saturation image.

A computer-implemented method of predicting a status of an embolization procedure on an aneurism, includes: receiving (S110) projection image data (110) representing temporal blood flow in a region of the anatomy including the aneurism during the embolization procedure; inputting (S120) the received projection image data (110) into a neural network (120) trained to predict temporal blood flow (130), wherein the neural network (120) is trained to predict temporal blood flow (130) using training data (140) representing temporal blood flow in a region of the anatomy that does not include an aneurism; and in response to the inputting (S120): generating (S130) an output (160) indicative of the status of the embolization procedure based on the predicted temporal blood flow.

An imaging chamber configured to receive a flow of a fluid sample is provided. The imaging chamber includes a reservoir and an imaging window. The reservoir includes a contrast-enhancing agent and is configured to receive the flow of the fluid sample. The imaging window of the imaging chamber is downstream from the reservoir.

An information processing device, an information processing method, and a computer-readable recording medium that are capable of generating, with a smaller amount of learning data, a learned model that infers a blood circulation anomalous area in a medical image are provided.

A learning unit 124 and a model output unit 126 are provided. The learning unit 124 is configured to cause a machine learning model 125 to learn by inputting medical images and blood vessel images into the machine learning model, the medical images being provided with annotation information of a blood circulation anomalous area, the blood vessel images being obtained by estimating a blood vessel area in the medical images based on the medical images. The model output unit 126 outputs a learned model having learned at the learning unit.